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Authors: Nazmul Takbir ; Tahmeed Tarek and Muhammad Adnan

Affiliation: Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh

Keyword(s): Online Machine Learning, Machine Learning, Serverless Computing, Stream Analytics.

Abstract: Recently, researchers have seen promising results in using serverless computing for real-time machine learning inference tasks. Several researchers have also used serverless for machine learning training and compared it against VM-based (virtual machine) training. However, most of these approaches, which assumed traditional offline machine learning, did not find serverless to be particularly useful for model training. In our work, we take a different approach; we explore online machine learning. The incremental nature of training online machine learning models allows better utilization of the elastic scaling and consumption-based pricing offered by serverless. Hence, we introduce Creek, a proof-of-concept system for training online machine learning models on streaming data using serverless. We explore architectural variants of Creek on AWS and compare them in terms of monetary cost and training latency. We also compare Creek against VM-based training and identify the factors influenc ing the choice between a serverless and VM-based solution. We explore model parallelism and introduce a usage-based dynamic memory allocation of serverless functions to reduce costs. Our results indicate that serverless training is cheaper than VM-based training when the streaming rate is sporadic and unpredictable. Furthermore, parallel training using serverless can significantly reduce training latency for models with low communication overhead. (More)

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Paper citation in several formats:
Takbir, N., Tarek, T. and Adnan, M. (2024). Creek: Leveraging Serverless for Online Machine Learning on Streaming Data. In Proceedings of the 14th International Conference on Cloud Computing and Services Science - CLOSER; ISBN 978-989-758-701-6; ISSN 2184-5042, SciTePress, pages 38-49. DOI: 10.5220/0012619100003711

@conference{closer24,
author={Nazmul Takbir and Tahmeed Tarek and Muhammad Adnan},
title={Creek: Leveraging Serverless for Online Machine Learning on Streaming Data},
booktitle={Proceedings of the 14th International Conference on Cloud Computing and Services Science - CLOSER},
year={2024},
pages={38-49},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012619100003711},
isbn={978-989-758-701-6},
issn={2184-5042},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Cloud Computing and Services Science - CLOSER
TI - Creek: Leveraging Serverless for Online Machine Learning on Streaming Data
SN - 978-989-758-701-6
IS - 2184-5042
AU - Takbir, N.
AU - Tarek, T.
AU - Adnan, M.
PY - 2024
SP - 38
EP - 49
DO - 10.5220/0012619100003711
PB - SciTePress

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